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Thursday, 06 June 2024 08:41

The trio of technologies boosting the capability of software engineering teams

By Dynatrace
Rafi Katanasho, APAC Chief Technology Officer at Dynatrace Rafi Katanasho, APAC Chief Technology Officer at Dynatrace

GUEST OPINION:  While many eyes are on generative AI to find coding efficiencies, sizable efficiency gains are still possible with augmentation of existing DevOps practices.

The efficiency of software engineering functions has been firmly in the spotlight in the early part of this year, as coders emerge as a key user cohort for emerging generative AI-powered ‘pair-programming’ tools that promise efficiency and capability uplifts.

While there are early gains and promising signs from this work, it’s an improvement initiative that is fraught. Some 95% of technology leaders are concerned that using generative AI to create code could result in leakage and improper or illegal use of intellectual property; and the same proportion think the technology would be more beneficial if enriched and prompted by other types of AI that can provide precise facts about current states and accurate predictions about the future.

So, it appears for now at least, chasing software engineering efficiency gains with generative AI may not be the most effective strategy.

The level of experimentation does, however, show there is appetite among organisations for further enhancement and improvement to existing engineering functions and practices.

Importantly for organisations, there are other ways available to make these improvements.

One way is by renewing focus on existing engineering best practices that are already in use, such as DevOps, where considerable optimisation potential still exists.

As many organisations have discovered in the 15 or so years since DevOps was conceived, the shift to DevOps is critical to support the ever-accelerating development speeds that customers and internal stakeholders demand. Combining development with operations and the processes that support them enables organisations to keep pace with the speed of development. More than that, DevOps has brought about a cultural shift within organisations that has fundamentally changed the way every stage of the software development process is approached.

But while DevOps is providing immense value as it is, its efficacy and output can be significantly bolstered through additional technological investments in observability, application security, and AI.

Together, these can improve the reliability, security, and efficiency of DevOps implementations and code output.

Adding observability

Observability, along with DevOps, empowers teams to embrace a shift-left approach. The deep visibility and insights that observability provides allow teams to take proactive measures early in the software development life cycle (SDLC).

Shifting left is an approach that includes software quality, performance, and security testing as a part of the SDLC. These practices ensure optimal software functioning and the quick resolution of issues before they proliferate. This practice has become integral to DevOps (and to variations such as DevSecOps), which fosters a culture of shared responsibility where all stakeholders play a role in maintaining the integrity of the software and infrastructure.

Without observability, a DevOps approach becomes increasingly difficult to execute. A lack of insights and visibility into a digital environment leads to inefficient management and resolution of vulnerabilities, attacks, and threats. Conversely, with observability providing a clear explanation of the root cause or origin of security issues, teams can immediately begin addressing issues. Ultimately, observability-powered insights preserve resources and enable DevOps at scale.

Observability is also a launchpad for maturing DevOps postures. Recent research found that 71% of organisations actively use observability data and insights to drive automation decisions and improvements in DevOps workflows. The technology has also enabled 78% of organisations to automate release validation and 74% of organisations to identify bottlenecks and automate delivery pipelines.

However, observability remains only one piece of the puzzle when it comes to ensuring the success of DevOps.

More roles for more types of AI 

AI investments can also play a key role in transforming software engineering. When integrated into DevOps or DevSecOps, AI helps teams transform data into an actionable asset for automating workflows across development, security, and operations.

In particular, combining causal AI with machine learning-based algorithms analyses vast datasets in real-time and provides practitioners with precise answers driven by root cause analysis.

This capability is monumental for DevOps teams. The AI helps provide in-depth context around system issues, anomalies, and other events instead of merely identifying them. Without this level of context, datasets become exponentially difficult to analyse and use for any effective or efficient DevOps processes. Causal AI also bolsters DevOps by allowing for early anomaly and vulnerability detection, rapid issue resolution, and system performance optimisation.

In addition, informed by past performances, predictive AI paired with observability forecasts future system needs and offers predictive insights. This fosters a proactive approach to system health and maintenance. These capabilities enable technical teams to minimise disruptions, cyberattacks, and downtime by identifying potential issues before they escalate.

Finally, AI can be used to strengthen code security, not only by offering continuous real-time insights into system vulnerabilities but also by providing intelligence and answers regarding future potential vulnerabilities.

Together, the synergy of observability, security, and AI redefines DevOps.These three capabilities are a recipe for accelerating software delivery and fortifying tech stacks and applications against emerging security challenges. This combination provides organisations with a way to uplift their software engineering practices and get more efficiency from current practices and structures.

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